Systems | Development | Analytics | API | Testing

Real-time analytics has become a cornerstone of modern enterprises. Businesses are no longer satisfied with waiting hours or days for insights—they demand answers in seconds. The rise of AI, machine learning, and generative AI has only accelerated this need, putting immense pressure on data platforms to deliver reliable, scalable, and flexible architectures.

Healthcare organizations are under growing pressure to connect legacy EHR (Electronic Health Record) and ERP (Enterprise Resource Planning) systems while safeguarding patient privacy and meeting strict compliance standards.

You’re presenting AI-generated analysis in your quarterly strategy meeting. The slides are polished, the insights look solid, and you’re ready to move the conversation forward. Then the CFO leans forward: “Where did this number come from? I reviewed this data last week and something doesn’t add up.”

Fivetran usage and pricing made simple — understand MAR (monthly active rows), syncs, and what drives your data costs.

To better guide our roadmap, Fivetran uses revenue-weighted feature usage (RWFU) — a metric that shifts the focus from raw adoption to real business impact.

You’ve clicked on a link, and you wait. And wait. And wait. You wouldn't stay, and neither would your customers. Slow applications are more than just a minor issue in the cutthroat digital world of today; they may harm your brand, user loyalty, and revenue. This is where performance testing comes in. It's not a single, isolated step but a critical, ongoing practice that runs across the entire software development lifecycle (SDLC).

October marks Cybersecurity Awareness Month. It’s a timely reminder that the files you touch, share, and rely on every day often hold your most valuable data and are exactly what attackers are after. Unstructured data, like spreadsheets, documents, logs, and backups, makes up the majority of your business data. That’s what makes it the prime target for ransomware.

Imagine an airline system monitoring traffic around an airport. If it detects a major delay, countless systems may need to react instantly: Ground operations to adjust flows. Some of these systems will still connect via API, traditional MQ or iPaaS technologies, but the data’s volume and urgency and the ease of decoupling apps make architecting with Kafka the better fit. The natural question is: should all these applications & systems connect to the same Kafka cluster?